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      "name": "FinRL_portfolio_allocation_NeurIPS_2020.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_portfolio_allocation_NeurIPS_2020.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yv3IDvrobU37"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Portfolio Allocation\n",
        "\n",
        "Tutorials to use OpenAI DRL to perform portfolio allocation in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: \n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kHCfEiTA80V"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUmLTmoQA7_w"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12v1i0jVkg48"
      },
      "source": [
        "<a id='0'></a>\r\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L63HKnWvkirx"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\r\n",
        "\r\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\r\n",
        "\r\n",
        "\r\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\r\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\r\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\r\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\r\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\r\n",
        "\r\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\r\n",
        "values at state s′ and s, respectively\r\n",
        "\r\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\r\n",
        "our trading agent observes many different features to better learn in an interactive environment.\r\n",
        "\r\n",
        "* Environment: Dow 30 consituents\r\n",
        "\r\n",
        "\r\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_emqQCCklVt"
      },
      "source": [
        "<a id='1'></a>\r\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVCcCalAknGn"
      },
      "source": [
        "<a id='1.1'></a>\r\n",
        "## 2.1. Install all the packages through FinRL library\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pT8a0fvhA_TW",
        "outputId": "e6c27e88-595a-430e-f27b-0e66741eb1a6"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard; extra == \"extra\"->stable-baselines3[extra]->finrl==0.0.3) (0.4.8)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.3-cp37-none-any.whl size=38201 sha256=680913f069c396f38e0c508600b450102190f08e0b0bba53c58c334981ccbe6c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-a1bbwmjm/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22616 sha256=2a578f51d56d3d8fff23683c041d6815f487abf3c6c97d4739d122055a6599b3\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75764 sha256=7e1ceb3360e57235c3d97bdbb36969c8ac05da709aa781413f1eca9088669323\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-a1bbwmjm/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39764 sha256=6b772c8c03b900a08799fdd831ee627277cc2c9241dc3103e2602fdd21781bb1\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.0.3 int-date-0.1.8 lxml-4.6.2 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-0.10.0 stockstats-0.3.2 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2568cp5bU38"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNmvYN9YbU4B"
      },
      "source": [
        "<a id='1.3'></a>\r\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntfTb0e2bU4C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef80e76b-89a1-428a-a5b1-5f1bf1d8100a"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_portfolio import StockPortfolioEnv\n",
        "\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import backtest_stats, backtest_plot, get_daily_return, get_baseline,convert_daily_return_to_pyfolio_ts\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlIS2abxkwan"
      },
      "source": [
        "<a id='1.4'></a>\r\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8bBq7nsBCfF"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slBria_QbU4F"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPsuy6d9yRPp",
        "outputId": "1f22c76f-dfd9-48c7-dfdc-f775ea436302"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEwzMkFHbU4G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf8f3155-a53b-4fc5-fd9c-4d4a6ab82b84"
      },
      "source": [
        "df = YahooDownloader(start_date = '2008-01-01',\n",
        "                     end_date = '2021-01-01',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (98167, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1xC-LpbbU4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "606fceb2-87fe-43d2-90a7-79a873964126"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>7.116786</td>\n",
              "      <td>7.152143</td>\n",
              "      <td>6.876786</td>\n",
              "      <td>5.993858</td>\n",
              "      <td>1079178800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>52.090000</td>\n",
              "      <td>52.320000</td>\n",
              "      <td>50.790001</td>\n",
              "      <td>41.025105</td>\n",
              "      <td>8053700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>87.570000</td>\n",
              "      <td>87.839996</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>63.481632</td>\n",
              "      <td>4303000</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>72.559998</td>\n",
              "      <td>72.669998</td>\n",
              "      <td>70.050003</td>\n",
              "      <td>48.370781</td>\n",
              "      <td>6337800</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>27.299999</td>\n",
              "      <td>26.209999</td>\n",
              "      <td>19.847456</td>\n",
              "      <td>64338900</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...      volume   tic  day\n",
              "0  2008-01-02   7.116786   7.152143  ...  1079178800  AAPL    2\n",
              "1  2008-01-02  52.090000  52.320000  ...     8053700   AXP    2\n",
              "2  2008-01-02  87.570000  87.839996  ...     4303000    BA    2\n",
              "3  2008-01-02  72.559998  72.669998  ...     6337800   CAT    2\n",
              "4  2008-01-02  27.000000  27.299999  ...    64338900  CSCO    2\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS1-nxRzbU4i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14ccb74b-3f20-434c-f130-5d5fb58058f1"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98167, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9UwKwzRbU4l"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5h8RbeBHMDQ",
        "outputId": "d412683b-0424-4ddf-b23b-056eec0c2c1f"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    use_turbulence=False,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "df = fe.preprocess_data(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zsIW1LAiqCb",
        "outputId": "006148f2-20fa-4eb8-cf45-02334e76b1ad"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(98167, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWB-RoTSbU4s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "d464d41f-f975-4ffd-8800-79e7a7c605e4"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-01-02</td>\n",
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              "      <td>6.876786</td>\n",
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              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.993858</td>\n",
              "      <td>5.993858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>52.090000</td>\n",
              "      <td>52.320000</td>\n",
              "      <td>50.790001</td>\n",
              "      <td>41.025105</td>\n",
              "      <td>8053700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000062</td>\n",
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              "      <td>-66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.995243</td>\n",
              "      <td>5.995243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>87.570000</td>\n",
              "      <td>87.839996</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>63.481632</td>\n",
              "      <td>4303000</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.014116</td>\n",
              "      <td>6.370096</td>\n",
              "      <td>5.316144</td>\n",
              "      <td>0.581347</td>\n",
              "      <td>-100.000000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.843120</td>\n",
              "      <td>5.843120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>72.559998</td>\n",
              "      <td>72.669998</td>\n",
              "      <td>70.050003</td>\n",
              "      <td>48.370781</td>\n",
              "      <td>6337800</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.022895</td>\n",
              "      <td>6.321507</td>\n",
              "      <td>5.175541</td>\n",
              "      <td>0.498339</td>\n",
              "      <td>-98.325887</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.748524</td>\n",
              "      <td>5.748524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-01-02</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>27.299999</td>\n",
              "      <td>26.209999</td>\n",
              "      <td>19.847456</td>\n",
              "      <td>64338900</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "      <td>-0.035006</td>\n",
              "      <td>6.308829</td>\n",
              "      <td>4.996074</td>\n",
              "      <td>0.358091</td>\n",
              "      <td>-86.835896</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.652452</td>\n",
              "      <td>5.652452</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...  dx_30  close_30_sma  close_60_sma\n",
              "0  2008-01-02   7.116786   7.152143  ...  100.0      5.993858      5.993858\n",
              "1  2008-01-02  52.090000  52.320000  ...  100.0      5.995243      5.995243\n",
              "2  2008-01-02  87.570000  87.839996  ...  100.0      5.843120      5.843120\n",
              "3  2008-01-02  72.559998  72.669998  ...  100.0      5.748524      5.748524\n",
              "4  2008-01-02  27.000000  27.299999  ...  100.0      5.652452      5.652452\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qz9K2vul6RmK"
      },
      "source": [
        "## Add covariance matrix as states"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhizvNwcrg1n"
      },
      "source": [
        "# add covariance matrix as states\n",
        "df=df.sort_values(['date','tic'],ignore_index=True)\n",
        "df.index = df.date.factorize()[0]\n",
        "\n",
        "cov_list = []\n",
        "# look back is one year\n",
        "lookback=252\n",
        "for i in range(lookback,len(df.index.unique())):\n",
        "  data_lookback = df.loc[i-lookback:i,:]\n",
        "  price_lookback=data_lookback.pivot_table(index = 'date',columns = 'tic', values = 'close')\n",
        "  return_lookback = price_lookback.pct_change().dropna()\n",
        "  covs = return_lookback.cov().values \n",
        "  cov_list.append(covs)\n",
        "  \n",
        "df_cov = pd.DataFrame({'date':df.date.unique()[lookback:],'cov_list':cov_list})\n",
        "df = df.merge(df_cov, on='date')\n",
        "df = df.sort_values(['date','tic']).reset_index(drop=True)\n",
        "        "
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUPwwa13uBQ-",
        "outputId": "909bf878-a9b5-4cef-c4c7-d52a0ccfb61c"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90660, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "wv3jR1zPrg4g",
        "outputId": "7cb164ec-1def-4aa0-d99f-fd00ff154cbb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>cov_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>3.070357</td>\n",
              "      <td>3.133571</td>\n",
              "      <td>3.047857</td>\n",
              "      <td>2.625620</td>\n",
              "      <td>607541200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>2</td>\n",
              "      <td>0.112284</td>\n",
              "      <td>53.286016</td>\n",
              "      <td>47.561928</td>\n",
              "      <td>48.009267</td>\n",
              "      <td>25.841124</td>\n",
              "      <td>5.858025</td>\n",
              "      <td>50.614908</td>\n",
              "      <td>50.970387</td>\n",
              "      <td>[[0.0014139107044444073, 0.0011800730357944612...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>17.969999</td>\n",
              "      <td>18.750000</td>\n",
              "      <td>17.910000</td>\n",
              "      <td>15.110051</td>\n",
              "      <td>9625600</td>\n",
              "      <td>AXP</td>\n",
              "      <td>2</td>\n",
              "      <td>0.225512</td>\n",
              "      <td>53.469344</td>\n",
              "      <td>47.791641</td>\n",
              "      <td>51.045341</td>\n",
              "      <td>80.048691</td>\n",
              "      <td>0.220406</td>\n",
              "      <td>50.690795</td>\n",
              "      <td>50.894582</td>\n",
              "      <td>[[0.0014139107044444073, 0.0011800730357944612...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>41.590000</td>\n",
              "      <td>43.049999</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>32.005901</td>\n",
              "      <td>5443100</td>\n",
              "      <td>BA</td>\n",
              "      <td>2</td>\n",
              "      <td>0.322032</td>\n",
              "      <td>53.717290</td>\n",
              "      <td>47.766477</td>\n",
              "      <td>51.269660</td>\n",
              "      <td>96.444709</td>\n",
              "      <td>2.969231</td>\n",
              "      <td>50.754757</td>\n",
              "      <td>50.823502</td>\n",
              "      <td>[[0.0014139107044444073, 0.0011800730357944612...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>45.099998</td>\n",
              "      <td>43.700001</td>\n",
              "      <td>31.403988</td>\n",
              "      <td>6277400</td>\n",
              "      <td>CAT</td>\n",
              "      <td>2</td>\n",
              "      <td>0.327176</td>\n",
              "      <td>53.791300</td>\n",
              "      <td>47.768897</td>\n",
              "      <td>49.751662</td>\n",
              "      <td>71.687120</td>\n",
              "      <td>0.296878</td>\n",
              "      <td>50.778336</td>\n",
              "      <td>50.740350</td>\n",
              "      <td>[[0.0014139107044444073, 0.0011800730357944612...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-12-31</td>\n",
              "      <td>16.180000</td>\n",
              "      <td>16.549999</td>\n",
              "      <td>16.120001</td>\n",
              "      <td>12.189656</td>\n",
              "      <td>37513700</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>2</td>\n",
              "      <td>0.511681</td>\n",
              "      <td>54.318893</td>\n",
              "      <td>47.618570</td>\n",
              "      <td>53.664757</td>\n",
              "      <td>172.384973</td>\n",
              "      <td>17.964888</td>\n",
              "      <td>50.874008</td>\n",
              "      <td>50.731082</td>\n",
              "      <td>[[0.0014139107044444073, 0.0011800730357944612...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2008-12-31  ...  [[0.0014139107044444073, 0.0011800730357944612...\n",
              "1  2008-12-31  ...  [[0.0014139107044444073, 0.0011800730357944612...\n",
              "2  2008-12-31  ...  [[0.0014139107044444073, 0.0011800730357944612...\n",
              "3  2008-12-31  ...  [[0.0014139107044444073, 0.0011800730357944612...\n",
              "4  2008-12-31  ...  [[0.0014139107044444073, 0.0011800730357944612...\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UooHj1OgbU4v"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQnmN1qdk88I"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrPxgv4eBQ_R"
      },
      "source": [
        "train = data_split(df, '2009-01-01','2019-01-01')\n",
        "#trade = data_split(df, '2020-01-01', config.END_DATE)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "vU2vXEll0hfk",
        "outputId": "b734abd2-aeb6-495b-d379-61012eb04b1c"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>cov_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "      <td>0.916411</td>\n",
              "      <td>58.037324</td>\n",
              "      <td>50.483560</td>\n",
              "      <td>58.279413</td>\n",
              "      <td>231.362748</td>\n",
              "      <td>28.830569</td>\n",
              "      <td>53.921125</td>\n",
              "      <td>52.268017</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.745411</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "      <td>1.104879</td>\n",
              "      <td>58.632931</td>\n",
              "      <td>50.354922</td>\n",
              "      <td>57.951206</td>\n",
              "      <td>206.221389</td>\n",
              "      <td>31.974391</td>\n",
              "      <td>54.109576</td>\n",
              "      <td>52.400186</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "      <td>1.225587</td>\n",
              "      <td>59.109112</td>\n",
              "      <td>50.287868</td>\n",
              "      <td>57.599664</td>\n",
              "      <td>171.846491</td>\n",
              "      <td>31.974391</td>\n",
              "      <td>54.287689</td>\n",
              "      <td>52.521223</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.978760</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "      <td>1.288569</td>\n",
              "      <td>59.477777</td>\n",
              "      <td>50.242415</td>\n",
              "      <td>57.159546</td>\n",
              "      <td>138.088204</td>\n",
              "      <td>22.759063</td>\n",
              "      <td>54.486354</td>\n",
              "      <td>52.632345</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.683227</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "      <td>1.295162</td>\n",
              "      <td>59.737547</td>\n",
              "      <td>50.442506</td>\n",
              "      <td>56.448825</td>\n",
              "      <td>117.945877</td>\n",
              "      <td>22.759063</td>\n",
              "      <td>54.596320</td>\n",
              "      <td>52.735164</td>\n",
              "      <td>[[0.001427708918378416, 0.0011886537539480882,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  ...                                           cov_list\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "0  2009-01-02  ...  [[0.001427708918378416, 0.0011886537539480882,...\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxQTNjpblAMN"
      },
      "source": [
        "## Environment for Portfolio Allocation\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlfE-VERbU40"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym.utils import seeding\n",
        "import gym\n",
        "from gym import spaces\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "from stable_baselines3.common.vec_env import DummyVecEnv\n",
        "\n",
        "\n",
        "class StockPortfolioEnv(gym.Env):\n",
        "    \"\"\"A single stock trading environment for OpenAI gym\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            input data\n",
        "        stock_dim : int\n",
        "            number of unique stocks\n",
        "        hmax : int\n",
        "            maximum number of shares to trade\n",
        "        initial_amount : int\n",
        "            start money\n",
        "        transaction_cost_pct: float\n",
        "            transaction cost percentage per trade\n",
        "        reward_scaling: float\n",
        "            scaling factor for reward, good for training\n",
        "        state_space: int\n",
        "            the dimension of input features\n",
        "        action_space: int\n",
        "            equals stock dimension\n",
        "        tech_indicator_list: list\n",
        "            a list of technical indicator names\n",
        "        turbulence_threshold: int\n",
        "            a threshold to control risk aversion\n",
        "        day: int\n",
        "            an increment number to control date\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    _sell_stock()\n",
        "        perform sell action based on the sign of the action\n",
        "    _buy_stock()\n",
        "        perform buy action based on the sign of the action\n",
        "    step()\n",
        "        at each step the agent will return actions, then \n",
        "        we will calculate the reward, and return the next observation.\n",
        "    reset()\n",
        "        reset the environment\n",
        "    render()\n",
        "        use render to return other functions\n",
        "    save_asset_memory()\n",
        "        return account value at each time step\n",
        "    save_action_memory()\n",
        "        return actions/positions at each time step\n",
        "        \n",
        "\n",
        "    \"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, \n",
        "                df,\n",
        "                stock_dim,\n",
        "                hmax,\n",
        "                initial_amount,\n",
        "                transaction_cost_pct,\n",
        "                reward_scaling,\n",
        "                state_space,\n",
        "                action_space,\n",
        "                tech_indicator_list,\n",
        "                turbulence_threshold=None,\n",
        "                lookback=252,\n",
        "                day = 0):\n",
        "        #super(StockEnv, self).__init__()\n",
        "        #money = 10 , scope = 1\n",
        "        self.day = day\n",
        "        self.lookback=lookback\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.initial_amount = initial_amount\n",
        "        self.transaction_cost_pct =transaction_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "\n",
        "        # action_space normalization and shape is self.stock_dim\n",
        "        self.action_space = spaces.Box(low = 0, high = 1,shape = (self.action_space,)) \n",
        "        # Shape = (34, 30)\n",
        "        # covariance matrix + technical indicators\n",
        "        self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape = (self.state_space+len(self.tech_indicator_list),self.state_space))\n",
        "\n",
        "        # load data from a pandas dataframe\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.terminal = False     \n",
        "        self.turbulence_threshold = turbulence_threshold        \n",
        "        # initalize state: inital portfolio return + individual stock return + individual weights\n",
        "        self.portfolio_value = self.initial_amount\n",
        "\n",
        "        # memorize portfolio value each step\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        # memorize portfolio return each step\n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]]\n",
        "\n",
        "        \n",
        "    def step(self, actions):\n",
        "        # print(self.day)\n",
        "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
        "        # print(actions)\n",
        "\n",
        "        if self.terminal:\n",
        "            df = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df.columns = ['daily_return']\n",
        "            plt.plot(df.daily_return.cumsum(),'r')\n",
        "            plt.savefig('results/cumulative_reward.png')\n",
        "            plt.close()\n",
        "            \n",
        "            plt.plot(self.portfolio_return_memory,'r')\n",
        "            plt.savefig('results/rewards.png')\n",
        "            plt.close()\n",
        "\n",
        "            print(\"=================================\")\n",
        "            print(\"begin_total_asset:{}\".format(self.asset_memory[0]))           \n",
        "            print(\"end_total_asset:{}\".format(self.portfolio_value))\n",
        "\n",
        "            df_daily_return = pd.DataFrame(self.portfolio_return_memory)\n",
        "            df_daily_return.columns = ['daily_return']\n",
        "            if df_daily_return['daily_return'].std() !=0:\n",
        "              sharpe = (252**0.5)*df_daily_return['daily_return'].mean()/ \\\n",
        "                       df_daily_return['daily_return'].std()\n",
        "              print(\"Sharpe: \",sharpe)\n",
        "            print(\"=================================\")\n",
        "            \n",
        "            return self.state, self.reward, self.terminal,{}\n",
        "\n",
        "        else:\n",
        "            #print(\"Model actions: \",actions)\n",
        "            # actions are the portfolio weight\n",
        "            # normalize to sum of 1\n",
        "            #if (np.array(actions) - np.array(actions).min()).sum() != 0:\n",
        "            #  norm_actions = (np.array(actions) - np.array(actions).min()) / (np.array(actions) - np.array(actions).min()).sum()\n",
        "            #else:\n",
        "            #  norm_actions = actions\n",
        "            weights = self.softmax_normalization(actions) \n",
        "            #print(\"Normalized actions: \", weights)\n",
        "            self.actions_memory.append(weights)\n",
        "            last_day_memory = self.data\n",
        "\n",
        "            #load next state\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day,:]\n",
        "            self.covs = self.data['cov_list'].values[0]\n",
        "            self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "            #print(self.state)\n",
        "            # calcualte portfolio return\n",
        "            # individual stocks' return * weight\n",
        "            portfolio_return = sum(((self.data.close.values / last_day_memory.close.values)-1)*weights)\n",
        "            # update portfolio value\n",
        "            new_portfolio_value = self.portfolio_value*(1+portfolio_return)\n",
        "            self.portfolio_value = new_portfolio_value\n",
        "\n",
        "            # save into memory\n",
        "            self.portfolio_return_memory.append(portfolio_return)\n",
        "            self.date_memory.append(self.data.date.unique()[0])            \n",
        "            self.asset_memory.append(new_portfolio_value)\n",
        "\n",
        "            # the reward is the new portfolio value or end portfolo value\n",
        "            self.reward = new_portfolio_value \n",
        "            #print(\"Step reward: \", self.reward)\n",
        "            #self.reward = self.reward*self.reward_scaling\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        # load states\n",
        "        self.covs = self.data['cov_list'].values[0]\n",
        "        self.state =  np.append(np.array(self.covs), [self.data[tech].values.tolist() for tech in self.tech_indicator_list ], axis=0)\n",
        "        self.portfolio_value = self.initial_amount\n",
        "        #self.cost = 0\n",
        "        #self.trades = 0\n",
        "        self.terminal = False \n",
        "        self.portfolio_return_memory = [0]\n",
        "        self.actions_memory=[[1/self.stock_dim]*self.stock_dim]\n",
        "        self.date_memory=[self.data.date.unique()[0]] \n",
        "        return self.state\n",
        "    \n",
        "    def render(self, mode='human'):\n",
        "        return self.state\n",
        "        \n",
        "    def softmax_normalization(self, actions):\n",
        "        numerator = np.exp(actions)\n",
        "        denominator = np.sum(np.exp(actions))\n",
        "        softmax_output = numerator/denominator\n",
        "        return softmax_output\n",
        "\n",
        "    \n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        portfolio_return = self.portfolio_return_memory\n",
        "        #print(len(date_list))\n",
        "        #print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'daily_return':portfolio_return})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = self.date_memory\n",
        "        df_date = pd.DataFrame(date_list)\n",
        "        df_date.columns = ['date']\n",
        "        \n",
        "        action_list = self.actions_memory\n",
        "        df_actions = pd.DataFrame(action_list)\n",
        "        df_actions.columns = self.data.tic.values\n",
        "        df_actions.index = df_date.date\n",
        "        #df_actions = pd.DataFrame({'date':date_list,'actions':action_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]\n",
        "\n",
        "    def get_sb_env(self):\n",
        "        e = DummyVecEnv([lambda: self])\n",
        "        obs = e.reset()\n",
        "        return e, obs"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzD06X0CbU43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38e42305-b481-449c-bee8-a8e11fede284"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyg0_ZuVEVQ5"
      },
      "source": [
        "env_kwargs = {\r\n",
        "    \"hmax\": 100, \r\n",
        "    \"initial_amount\": 1000000, \r\n",
        "    \"transaction_cost_pct\": 0.001, \r\n",
        "    \"state_space\": state_space, \r\n",
        "    \"stock_dim\": stock_dimension, \r\n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \r\n",
        "    \"action_space\": stock_dimension, \r\n",
        "    \"reward_scaling\": 1e-4\r\n",
        "    \r\n",
        "}\r\n",
        "\r\n",
        "e_train_gym = StockPortfolioEnv(df = train, **env_kwargs)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTlOf8SJGdkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9d0875c-4457-4833-8077-9feacdcc8f92"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eKIu5UPlPnk"
      },
      "source": [
        "<a id='5'></a>\r\n",
        "# Part 6: Implement DRL Algorithms\r\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\r\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\r\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\r\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDxU0iCEGdnb"
      },
      "source": [
        "# initialize\r\n",
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdPe8uzflbXe"
      },
      "source": [
        "### Model 1: **A2C**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1tORf1fIcQ2",
        "outputId": "80a5ad2b-4511-4e89-a1ee-4a6393755bde"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "\n",
        "A2C_PARAMS = {\"n_steps\": 5, \"ent_coef\": 0.005, \"learning_rate\": 0.0002}\n",
        "model_a2c = agent.get_model(model_name=\"a2c\",model_kwargs = A2C_PARAMS)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.005, 'learning_rate': 0.0002}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DazEdrMpIdyz",
        "outputId": "2ea4ccb7-36bb-4244-b1d9-52ad8b1034b8"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                                tb_log_name='a2c',\n",
        "                                total_timesteps=60000)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 130       |\n",
            "|    iterations         | 100       |\n",
            "|    time_elapsed       | 3         |\n",
            "|    total_timesteps    | 500       |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -4.23e+15 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 99        |\n",
            "|    policy_loss        | 1.8e+08   |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 2.48e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 157       |\n",
            "|    iterations         | 200       |\n",
            "|    time_elapsed       | 6         |\n",
            "|    total_timesteps    | 1000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -7.89e+14 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 199       |\n",
            "|    policy_loss        | 2.44e+08  |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 4.08e+13  |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 167       |\n",
            "|    iterations         | 300       |\n",
            "|    time_elapsed       | 8         |\n",
            "|    total_timesteps    | 1500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -9.77e+25 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 299       |\n",
            "|    policy_loss        | 4.02e+08  |\n",
            "|    std                | 0.997     |\n",
            "|    value_loss         | 9.82e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 179      |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.5    |\n",
            "|    explained_variance | -6.9e+16 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | 4.57e+08 |\n",
            "|    std                | 0.997    |\n",
            "|    value_loss         | 1.39e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 189       |\n",
            "|    iterations         | 500       |\n",
            "|    time_elapsed       | 13        |\n",
            "|    total_timesteps    | 2500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.5     |\n",
            "|    explained_variance | -4.81e+17 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 499       |\n",
            "|    policy_loss        | 6.13e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 2.53e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4550666.315740787\n",
            "Sharpe:  1.0302838133559835\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 192      |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 15       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 1.96e+08 |\n",
            "|    std                | 0.996    |\n",
            "|    value_loss         | 2.53e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 197       |\n",
            "|    iterations         | 700       |\n",
            "|    time_elapsed       | 17        |\n",
            "|    total_timesteps    | 3500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -2.18e+17 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 699       |\n",
            "|    policy_loss        | 2.37e+08  |\n",
            "|    std                | 0.996     |\n",
            "|    value_loss         | 4.06e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 202      |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 19       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | 3.7e+08  |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 1.01e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 206      |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 21       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 4.31e+08 |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 1.29e+14 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 208       |\n",
            "|    iterations         | 1000      |\n",
            "|    time_elapsed       | 23        |\n",
            "|    total_timesteps    | 5000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.18e+18 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 999       |\n",
            "|    policy_loss        | 6.01e+08  |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 2.52e+14  |\n",
            "-------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4538927.251756459\n",
            "Sharpe:  1.0239597239761906\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 209      |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 26       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | 2.02e+08 |\n",
            "|    std                | 0.995    |\n",
            "|    value_loss         | 2.44e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 211       |\n",
            "|    iterations         | 1200      |\n",
            "|    time_elapsed       | 28        |\n",
            "|    total_timesteps    | 6000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -3.58e+18 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1199      |\n",
            "|    policy_loss        | 2.77e+08  |\n",
            "|    std                | 0.995     |\n",
            "|    value_loss         | 4.09e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 30       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 3.35e+08 |\n",
            "|    std                | 0.994    |\n",
            "|    value_loss         | 8.06e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 215       |\n",
            "|    iterations         | 1400      |\n",
            "|    time_elapsed       | 32        |\n",
            "|    total_timesteps    | 7000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.69e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1399      |\n",
            "|    policy_loss        | 4.1e+08   |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 1.2e+14   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 217      |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 34       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | 5.74e+08 |\n",
            "|    std                | 0.994    |\n",
            "|    value_loss         | 2.47e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4569623.286530429\n",
            "Sharpe:  1.0309827263626288\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 217       |\n",
            "|    iterations         | 1600      |\n",
            "|    time_elapsed       | 36        |\n",
            "|    total_timesteps    | 8000      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.4     |\n",
            "|    explained_variance | -1.11e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1599      |\n",
            "|    policy_loss        | 1.81e+08  |\n",
            "|    std                | 0.994     |\n",
            "|    value_loss         | 2.28e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 218      |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 38       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | 2.6e+08  |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 4.54e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 216      |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | 3.57e+08 |\n",
            "|    std                | 0.993    |\n",
            "|    value_loss         | 9.62e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 216       |\n",
            "|    iterations         | 1900      |\n",
            "|    time_elapsed       | 43        |\n",
            "|    total_timesteps    | 9500      |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -6.95e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 1899      |\n",
            "|    policy_loss        | 4.08e+08  |\n",
            "|    std                | 0.992     |\n",
            "|    value_loss         | 1.33e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 216      |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 46       |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | 7.22e+08 |\n",
            "|    std                | 0.991    |\n",
            "|    value_loss         | 3.02e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4784563.101868668\n",
            "Sharpe:  1.0546332869946304\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 216      |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 48       |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 1.64e+08 |\n",
            "|    std                | 0.991    |\n",
            "|    value_loss         | 2.02e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 217      |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 50       |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | 2.31e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 3.61e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 218      |\n",
            "|    iterations         | 2300     |\n",
            "|    time_elapsed       | 52       |\n",
            "|    total_timesteps    | 11500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2299     |\n",
            "|    policy_loss        | 3.07e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 7.81e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 219      |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 54       |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | 4.03e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 1.05e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 220      |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 56       |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | 5.57e+08 |\n",
            "|    std                | 0.99     |\n",
            "|    value_loss         | 2.27e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4265807.380536508\n",
            "Sharpe:  0.9867782700137868\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 219       |\n",
            "|    iterations         | 2600      |\n",
            "|    time_elapsed       | 59        |\n",
            "|    total_timesteps    | 13000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.3     |\n",
            "|    explained_variance | -3.35e+20 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 2599      |\n",
            "|    policy_loss        | 1.62e+08  |\n",
            "|    std                | 0.989     |\n",
            "|    value_loss         | 1.89e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 220      |\n",
            "|    iterations         | 2700     |\n",
            "|    time_elapsed       | 61       |\n",
            "|    total_timesteps    | 13500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2699     |\n",
            "|    policy_loss        | 2.56e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 4.37e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 221      |\n",
            "|    iterations         | 2800     |\n",
            "|    time_elapsed       | 63       |\n",
            "|    total_timesteps    | 14000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2799     |\n",
            "|    policy_loss        | 3.57e+08 |\n",
            "|    std                | 0.989    |\n",
            "|    value_loss         | 9.53e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 221      |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 65       |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | 4.31e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 1.42e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 222      |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 67       |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | 6.16e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 2.68e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4737187.266470802\n",
            "Sharpe:  1.048554781654813\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 222      |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 69       |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | 1.57e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 1.96e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 222      |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 71       |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | 2.45e+08 |\n",
            "|    std                | 0.988    |\n",
            "|    value_loss         | 3.58e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 223      |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 73       |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | 3.71e+08 |\n",
            "|    std                | 0.987    |\n",
            "|    value_loss         | 8.38e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 223      |\n",
            "|    iterations         | 3400     |\n",
            "|    time_elapsed       | 75       |\n",
            "|    total_timesteps    | 17000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3399     |\n",
            "|    policy_loss        | 3.89e+08 |\n",
            "|    std                | 0.987    |\n",
            "|    value_loss         | 1.19e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 223      |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 78       |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | 5.47e+08 |\n",
            "|    std                | 0.987    |\n",
            "|    value_loss         | 2.32e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4594345.465329124\n",
            "Sharpe:  1.0338662249918555\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 223       |\n",
            "|    iterations         | 3600      |\n",
            "|    time_elapsed       | 80        |\n",
            "|    total_timesteps    | 18000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.2     |\n",
            "|    explained_variance | -2.39e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3599      |\n",
            "|    policy_loss        | 1.56e+08  |\n",
            "|    std                | 0.987     |\n",
            "|    value_loss         | 1.98e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 82       |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | 2.45e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 3.78e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 224       |\n",
            "|    iterations         | 3800      |\n",
            "|    time_elapsed       | 84        |\n",
            "|    total_timesteps    | 19000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.1     |\n",
            "|    explained_variance | -1.11e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 3799      |\n",
            "|    policy_loss        | 3.75e+08  |\n",
            "|    std                | 0.986     |\n",
            "|    value_loss         | 9.09e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | 4.23e+08 |\n",
            "|    std                | 0.986    |\n",
            "|    value_loss         | 1.09e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 225      |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 88       |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | 5.46e+08 |\n",
            "|    std                | 0.985    |\n",
            "|    value_loss         | 2.21e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4537629.671792137\n",
            "Sharpe:  1.027306122996326\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.1    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | 1.76e+08 |\n",
            "|    std                | 0.985    |\n",
            "|    value_loss         | 1.96e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 225       |\n",
            "|    iterations         | 4200      |\n",
            "|    time_elapsed       | 93        |\n",
            "|    total_timesteps    | 21000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -4.27e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4199      |\n",
            "|    policy_loss        | 2.17e+08  |\n",
            "|    std                | 0.983     |\n",
            "|    value_loss         | 3.5e+13   |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 225       |\n",
            "|    iterations         | 4300      |\n",
            "|    time_elapsed       | 95        |\n",
            "|    total_timesteps    | 21500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42       |\n",
            "|    explained_variance | -9.61e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 4299      |\n",
            "|    policy_loss        | 3.36e+08  |\n",
            "|    std                | 0.982     |\n",
            "|    value_loss         | 7.88e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 225      |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 97       |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 3.9e+08  |\n",
            "|    std                | 0.982    |\n",
            "|    value_loss         | 1.09e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 226      |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 99       |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | 5.96e+08 |\n",
            "|    std                | 0.982    |\n",
            "|    value_loss         | 2.24e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4641050.148925118\n",
            "Sharpe:  1.035206741352005\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 226      |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 101      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 1.86e+08 |\n",
            "|    std                | 0.981    |\n",
            "|    value_loss         | 2.04e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 226      |\n",
            "|    iterations         | 4700     |\n",
            "|    time_elapsed       | 103      |\n",
            "|    total_timesteps    | 23500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4699     |\n",
            "|    policy_loss        | 2.4e+08  |\n",
            "|    std                | 0.981    |\n",
            "|    value_loss         | 4.09e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 226      |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 105      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | 3.69e+08 |\n",
            "|    std                | 0.981    |\n",
            "|    value_loss         | 9.69e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 226      |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 108      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | -5.9e+21 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | 4.46e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 1.36e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 226      |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 110      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | 6.05e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 2.56e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5080677.099515816\n",
            "Sharpe:  1.0970818985375046\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 225      |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 113      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | 1.7e+08  |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 2.24e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 115      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | 2.39e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 3.92e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 117      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42      |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | 3.24e+08 |\n",
            "|    std                | 0.98     |\n",
            "|    value_loss         | 8.04e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 120      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | -4.8e+21 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | 4.29e+08 |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 1.22e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 122      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | 5.4e+08  |\n",
            "|    std                | 0.979    |\n",
            "|    value_loss         | 2.31e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4811657.503165074\n",
            "Sharpe:  1.0589276474603557\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 124      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | 1.71e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 2.12e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 126      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 2.15e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 3.76e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 129      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | 3.25e+08 |\n",
            "|    std                | 0.978    |\n",
            "|    value_loss         | 7.21e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 131      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.9    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 3.48e+08 |\n",
            "|    std                | 0.977    |\n",
            "|    value_loss         | 9.82e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 225      |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 133      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 5.64e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 2.13e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4485060.270775738\n",
            "Sharpe:  1.01141473877631\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6100     |\n",
            "|    time_elapsed       | 135      |\n",
            "|    total_timesteps    | 30500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6099     |\n",
            "|    policy_loss        | 1.76e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 2.21e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 137      |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | 2.37e+08 |\n",
            "|    std                | 0.976    |\n",
            "|    value_loss         | 3.86e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 140      |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | 3.28e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 7.7e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 142      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 4.03e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 1.03e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 144      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 5.93e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 2.38e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4716704.9549536165\n",
            "Sharpe:  1.0510500905659037\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 147      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | 1.78e+08 |\n",
            "|    std                | 0.975    |\n",
            "|    value_loss         | 2.04e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 149      |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | 2.4e+08  |\n",
            "|    std                | 0.974    |\n",
            "|    value_loss         | 3.85e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 224       |\n",
            "|    iterations         | 6800      |\n",
            "|    time_elapsed       | 151       |\n",
            "|    total_timesteps    | 34000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.8     |\n",
            "|    explained_variance | -1.16e+24 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 6799      |\n",
            "|    policy_loss        | 3.2e+08   |\n",
            "|    std                | 0.974     |\n",
            "|    value_loss         | 7.66e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 153      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | 3.45e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 9.59e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 155      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.8    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 6.22e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 2.58e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4722061.646242311\n",
            "Sharpe:  1.0529486633467167\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 158      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | 1.63e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 1.91e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 160      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | 2.26e+08 |\n",
            "|    std                | 0.973    |\n",
            "|    value_loss         | 3.43e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 224      |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 162      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 3.31e+08 |\n",
            "|    std                | 0.972    |\n",
            "|    value_loss         | 7.69e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 223      |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 165      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 3.65e+08 |\n",
            "|    std                | 0.971    |\n",
            "|    value_loss         | 9.37e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 222      |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 168      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7499     |\n",
            "|    policy_loss        | 5.72e+08 |\n",
            "|    std                | 0.971    |\n",
            "|    value_loss         | 2.37e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4651172.332054012\n",
            "Sharpe:  1.0366825368944979\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 221      |\n",
            "|    iterations         | 7600     |\n",
            "|    time_elapsed       | 171      |\n",
            "|    total_timesteps    | 38000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.7    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7599     |\n",
            "|    policy_loss        | 1.71e+08 |\n",
            "|    std                | 0.971    |\n",
            "|    value_loss         | 2e+13    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 220      |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 174      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | 2e+08    |\n",
            "|    std                | 0.97     |\n",
            "|    value_loss         | 3.27e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 219      |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 177      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | -2.5e+23 |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | 3.23e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 8.21e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 218       |\n",
            "|    iterations         | 7900      |\n",
            "|    time_elapsed       | 181       |\n",
            "|    total_timesteps    | 39500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -3.76e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 7899      |\n",
            "|    policy_loss        | 4.25e+08  |\n",
            "|    std                | 0.969     |\n",
            "|    value_loss         | 1.23e+14  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 216      |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 184      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | 5.93e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 2.54e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5004208.576042484\n",
            "Sharpe:  1.0844189746438444\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 215      |\n",
            "|    iterations         | 8100     |\n",
            "|    time_elapsed       | 187      |\n",
            "|    total_timesteps    | 40500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8099     |\n",
            "|    policy_loss        | 1.66e+08 |\n",
            "|    std                | 0.969    |\n",
            "|    value_loss         | 2e+13    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 215       |\n",
            "|    iterations         | 8200      |\n",
            "|    time_elapsed       | 189       |\n",
            "|    total_timesteps    | 41000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -9.41e+22 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8199      |\n",
            "|    policy_loss        | 2.17e+08  |\n",
            "|    std                | 0.969     |\n",
            "|    value_loss         | 3.1e+13   |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 215       |\n",
            "|    iterations         | 8300      |\n",
            "|    time_elapsed       | 192       |\n",
            "|    total_timesteps    | 41500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.6     |\n",
            "|    explained_variance | -2.31e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8299      |\n",
            "|    policy_loss        | 3.37e+08  |\n",
            "|    std                | 0.968     |\n",
            "|    value_loss         | 7.5e+13   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 215      |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 194      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.6    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | 3.99e+08 |\n",
            "|    std                | 0.967    |\n",
            "|    value_loss         | 1.15e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 215      |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 197      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | 5.83e+08 |\n",
            "|    std                | 0.967    |\n",
            "|    value_loss         | 2.03e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4690651.093610478\n",
            "Sharpe:  1.0439707122222264\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 215       |\n",
            "|    iterations         | 8600      |\n",
            "|    time_elapsed       | 199       |\n",
            "|    total_timesteps    | 43000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -1.44e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8599      |\n",
            "|    policy_loss        | 1.58e+08  |\n",
            "|    std                | 0.967     |\n",
            "|    value_loss         | 1.95e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 215      |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 202      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | 2.11e+08 |\n",
            "|    std                | 0.966    |\n",
            "|    value_loss         | 3.08e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 215      |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 204      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | 3.28e+08 |\n",
            "|    std                | 0.965    |\n",
            "|    value_loss         | 7.03e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 214       |\n",
            "|    iterations         | 8900      |\n",
            "|    time_elapsed       | 207       |\n",
            "|    total_timesteps    | 44500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.5     |\n",
            "|    explained_variance | -3.36e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 8899      |\n",
            "|    policy_loss        | 4.06e+08  |\n",
            "|    std                | 0.965     |\n",
            "|    value_loss         | 1.1e+14   |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 210      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.5    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 8999     |\n",
            "|    policy_loss        | 5.2e+08  |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 1.98e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4660061.433540329\n",
            "Sharpe:  1.04048695684595\n",
            "=================================\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 213       |\n",
            "|    iterations         | 9100      |\n",
            "|    time_elapsed       | 213       |\n",
            "|    total_timesteps    | 45500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -1.77e+21 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9099      |\n",
            "|    policy_loss        | 1.62e+08  |\n",
            "|    std                | 0.964     |\n",
            "|    value_loss         | 1.83e+13  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 215      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | 2.01e+08 |\n",
            "|    std                | 0.964    |\n",
            "|    value_loss         | 2.87e+13 |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 213       |\n",
            "|    iterations         | 9300      |\n",
            "|    time_elapsed       | 217       |\n",
            "|    total_timesteps    | 46500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -41.4     |\n",
            "|    explained_variance | -2.13e+23 |\n",
            "|    learning_rate      | 0.0002    |\n",
            "|    n_updates          | 9299      |\n",
            "|    policy_loss        | 3.31e+08  |\n",
            "|    std                | 0.963     |\n",
            "|    value_loss         | 7e+13     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 220      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | 4.06e+08 |\n",
            "|    std                | 0.963    |\n",
            "|    value_loss         | 1.1e+14  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 222      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | 5.33e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 2.11e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4841177.689704771\n",
            "Sharpe:  1.0662304642107994\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 224      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | 1.42e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 1.54e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9700     |\n",
            "|    time_elapsed       | 226      |\n",
            "|    total_timesteps    | 48500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9699     |\n",
            "|    policy_loss        | 1.72e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 2.54e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 229      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | 3.05e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 6.27e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 213      |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 232      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9899     |\n",
            "|    policy_loss        | 3.52e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 9.87e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 234      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | 4.99e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 1.98e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4829593.807900699\n",
            "Sharpe:  1.0662441117803074\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10100    |\n",
            "|    time_elapsed       | 237      |\n",
            "|    total_timesteps    | 50500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10099    |\n",
            "|    policy_loss        | 1.41e+08 |\n",
            "|    std                | 0.962    |\n",
            "|    value_loss         | 1.59e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10200    |\n",
            "|    time_elapsed       | 239      |\n",
            "|    total_timesteps    | 51000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10199    |\n",
            "|    policy_loss        | 1.88e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 2.59e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10300    |\n",
            "|    time_elapsed       | 242      |\n",
            "|    total_timesteps    | 51500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10299    |\n",
            "|    policy_loss        | 3.11e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 5.9e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10400    |\n",
            "|    time_elapsed       | 244      |\n",
            "|    total_timesteps    | 52000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10399    |\n",
            "|    policy_loss        | 3.57e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 9.64e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10500    |\n",
            "|    time_elapsed       | 246      |\n",
            "|    total_timesteps    | 52500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.4    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10499    |\n",
            "|    policy_loss        | 4.69e+08 |\n",
            "|    std                | 0.961    |\n",
            "|    value_loss         | 1.89e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4867642.492651795\n",
            "Sharpe:  1.0695800575241914\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10600    |\n",
            "|    time_elapsed       | 249      |\n",
            "|    total_timesteps    | 53000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10599    |\n",
            "|    policy_loss        | 1.44e+08 |\n",
            "|    std                | 0.96     |\n",
            "|    value_loss         | 1.48e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10700    |\n",
            "|    time_elapsed       | 251      |\n",
            "|    total_timesteps    | 53500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10699    |\n",
            "|    policy_loss        | 1.9e+08  |\n",
            "|    std                | 0.96     |\n",
            "|    value_loss         | 2.62e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10800    |\n",
            "|    time_elapsed       | 253      |\n",
            "|    total_timesteps    | 54000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10799    |\n",
            "|    policy_loss        | 3.1e+08  |\n",
            "|    std                | 0.959    |\n",
            "|    value_loss         | 6.5e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 10900    |\n",
            "|    time_elapsed       | 256      |\n",
            "|    total_timesteps    | 54500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10899    |\n",
            "|    policy_loss        | 3.56e+08 |\n",
            "|    std                | 0.959    |\n",
            "|    value_loss         | 1.09e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 11000    |\n",
            "|    time_elapsed       | 258      |\n",
            "|    total_timesteps    | 55000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.3    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 10999    |\n",
            "|    policy_loss        | 4.86e+08 |\n",
            "|    std                | 0.958    |\n",
            "|    value_loss         | 1.8e+14  |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4722117.849533835\n",
            "Sharpe:  1.0511916286251552\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 11100    |\n",
            "|    time_elapsed       | 261      |\n",
            "|    total_timesteps    | 55500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11099    |\n",
            "|    policy_loss        | 1.37e+08 |\n",
            "|    std                | 0.957    |\n",
            "|    value_loss         | 1.42e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 11200    |\n",
            "|    time_elapsed       | 263      |\n",
            "|    total_timesteps    | 56000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11199    |\n",
            "|    policy_loss        | 2.17e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 3.5e+13  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 11300    |\n",
            "|    time_elapsed       | 265      |\n",
            "|    total_timesteps    | 56500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11299    |\n",
            "|    policy_loss        | 3.17e+08 |\n",
            "|    std                | 0.957    |\n",
            "|    value_loss         | 7.01e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 212      |\n",
            "|    iterations         | 11400    |\n",
            "|    time_elapsed       | 268      |\n",
            "|    total_timesteps    | 57000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11399    |\n",
            "|    policy_loss        | 3.67e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 1.15e+14 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 211      |\n",
            "|    iterations         | 11500    |\n",
            "|    time_elapsed       | 271      |\n",
            "|    total_timesteps    | 57500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11499    |\n",
            "|    policy_loss        | 5.1e+08  |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 1.78e+14 |\n",
            "------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4803878.457147342\n",
            "Sharpe:  1.0585455233591723\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 211      |\n",
            "|    iterations         | 11600    |\n",
            "|    time_elapsed       | 274      |\n",
            "|    total_timesteps    | 58000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11599    |\n",
            "|    policy_loss        | 1.22e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 1.16e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 211      |\n",
            "|    iterations         | 11700    |\n",
            "|    time_elapsed       | 276      |\n",
            "|    total_timesteps    | 58500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11699    |\n",
            "|    policy_loss        | 2.17e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 3.15e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 211      |\n",
            "|    iterations         | 11800    |\n",
            "|    time_elapsed       | 279      |\n",
            "|    total_timesteps    | 59000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11799    |\n",
            "|    policy_loss        | 3.13e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 6.62e+13 |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 211      |\n",
            "|    iterations         | 11900    |\n",
            "|    time_elapsed       | 281      |\n",
            "|    total_timesteps    | 59500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11899    |\n",
            "|    policy_loss        | 4.11e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 1.2e+14  |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 211      |\n",
            "|    iterations         | 12000    |\n",
            "|    time_elapsed       | 283      |\n",
            "|    total_timesteps    | 60000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -41.2    |\n",
            "|    explained_variance | nan      |\n",
            "|    learning_rate      | 0.0002   |\n",
            "|    n_updates          | 11999    |\n",
            "|    policy_loss        | 5.16e+08 |\n",
            "|    std                | 0.956    |\n",
            "|    value_loss         | 1.93e+14 |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvrqTro3lhAh"
      },
      "source": [
        "### Model 2: **PPO**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVXta7jVKMhV",
        "outputId": "4cb82ab6-914d-4d2b-a4ee-e47c8557dad3"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.005,\n",
        "    \"learning_rate\": 0.0001,\n",
        "    \"batch_size\": 128,\n",
        "}\n",
        "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.005, 'learning_rate': 0.0001, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5XlUIszKUGx",
        "outputId": "3989711a-cbed-4649-e71c-566d52917478"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_3\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 458  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 4    |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4917364.6278486075\n",
            "Sharpe:  1.074414829116363\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 391            |\n",
            "|    iterations           | 2              |\n",
            "|    time_elapsed         | 10             |\n",
            "|    total_timesteps      | 4096           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -7.8231096e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -3.71e+14      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 7.78e+14       |\n",
            "|    n_updates            | 10             |\n",
            "|    policy_gradient_loss | -6.16e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 1.57e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4996331.100586685\n",
            "Sharpe:  1.0890927964884638\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 373            |\n",
            "|    iterations           | 3              |\n",
            "|    time_elapsed         | 16             |\n",
            "|    total_timesteps      | 6144           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -3.5390258e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -8.76e+14      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.1e+15        |\n",
            "|    n_updates            | 20             |\n",
            "|    policy_gradient_loss | -4.29e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.33e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4751039.2878817525\n",
            "Sharpe:  1.0560179406423764\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 365            |\n",
            "|    iterations           | 4              |\n",
            "|    time_elapsed         | 22             |\n",
            "|    total_timesteps      | 8192           |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.6763806e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -8.01e+15      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.25e+15       |\n",
            "|    n_updates            | 30             |\n",
            "|    policy_gradient_loss | -5.58e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.59e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4769059.347696523\n",
            "Sharpe:  1.056814654380227\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 360            |\n",
            "|    iterations           | 5              |\n",
            "|    time_elapsed         | 28             |\n",
            "|    total_timesteps      | 10240          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -5.5879354e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -2.55e+16      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.24e+15       |\n",
            "|    n_updates            | 40             |\n",
            "|    policy_gradient_loss | -4.9e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.7e+15        |\n",
            "--------------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 358            |\n",
            "|    iterations           | 6              |\n",
            "|    time_elapsed         | 34             |\n",
            "|    total_timesteps      | 12288          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | 1.13621354e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -9.17e+16      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.35e+15       |\n",
            "|    n_updates            | 50             |\n",
            "|    policy_gradient_loss | -4.28e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.77e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4816491.86007194\n",
            "Sharpe:  1.0636199939613733\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 356           |\n",
            "|    iterations           | 7             |\n",
            "|    time_elapsed         | 40            |\n",
            "|    total_timesteps      | 14336         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.5390258e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.42e+17     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.03e+15      |\n",
            "|    n_updates            | 60            |\n",
            "|    policy_gradient_loss | -6.52e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.94e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4631919.83090099\n",
            "Sharpe:  1.0396504731290799\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 354           |\n",
            "|    iterations           | 8             |\n",
            "|    time_elapsed         | 46            |\n",
            "|    total_timesteps      | 16384         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.7508864e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -6.93e+17     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 9.83e+14      |\n",
            "|    n_updates            | 70            |\n",
            "|    policy_gradient_loss | -5.78e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.06e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4728763.286321457\n",
            "Sharpe:  1.052390302374202\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 353           |\n",
            "|    iterations           | 9             |\n",
            "|    time_elapsed         | 52            |\n",
            "|    total_timesteps      | 18432         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4703484e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.72e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.25e+15      |\n",
            "|    n_updates            | 80            |\n",
            "|    policy_gradient_loss | -4.84e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.33e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4439983.024798136\n",
            "Sharpe:  1.013829383303325\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 352            |\n",
            "|    iterations           | 10             |\n",
            "|    time_elapsed         | 58             |\n",
            "|    total_timesteps      | 20480          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.7e+18       |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.17e+15       |\n",
            "|    n_updates            | 90             |\n",
            "|    policy_gradient_loss | -4.82e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.58e+15       |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 352           |\n",
            "|    iterations           | 11            |\n",
            "|    time_elapsed         | 63            |\n",
            "|    total_timesteps      | 22528         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -9.313226e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.85e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.2e+15       |\n",
            "|    n_updates            | 100           |\n",
            "|    policy_gradient_loss | -5.2e-07      |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.51e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5048884.524536961\n",
            "Sharpe:  1.0963911876706685\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 351           |\n",
            "|    iterations           | 12            |\n",
            "|    time_elapsed         | 69            |\n",
            "|    total_timesteps      | 24576         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.7252903e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -2.67e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.44e+15      |\n",
            "|    n_updates            | 110           |\n",
            "|    policy_gradient_loss | -4.53e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.8e+15       |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4824229.456193555\n",
            "Sharpe:  1.0648549464252506\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 351           |\n",
            "|    iterations           | 13            |\n",
            "|    time_elapsed         | 75            |\n",
            "|    total_timesteps      | 26624         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 3.3527613e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -3.38e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 7.89e+14      |\n",
            "|    n_updates            | 120           |\n",
            "|    policy_gradient_loss | -6.06e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.76e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4602974.615591427\n",
            "Sharpe:  1.034753433280377\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 350           |\n",
            "|    iterations           | 14            |\n",
            "|    time_elapsed         | 81            |\n",
            "|    total_timesteps      | 28672         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 8.8475645e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.75e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.23e+15      |\n",
            "|    n_updates            | 130           |\n",
            "|    policy_gradient_loss | -5.8e-07      |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.27e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4608422.583401322\n",
            "Sharpe:  1.035300880612428\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 349           |\n",
            "|    iterations           | 15            |\n",
            "|    time_elapsed         | 87            |\n",
            "|    total_timesteps      | 30720         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3038516e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.71e+18     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.22e+15      |\n",
            "|    n_updates            | 140           |\n",
            "|    policy_gradient_loss | -5.63e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.39e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4826869.636472441\n",
            "Sharpe:  1.0676330284861433\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 348            |\n",
            "|    iterations           | 16             |\n",
            "|    time_elapsed         | 94             |\n",
            "|    total_timesteps      | 32768          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.4901161e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.51e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.22e+15       |\n",
            "|    n_updates            | 150            |\n",
            "|    policy_gradient_loss | -5.78e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.7e+15        |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 346           |\n",
            "|    iterations           | 17            |\n",
            "|    time_elapsed         | 100           |\n",
            "|    total_timesteps      | 34816         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -5.401671e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.48e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.48e+15      |\n",
            "|    n_updates            | 160           |\n",
            "|    policy_gradient_loss | -3.96e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.81e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4364006.929301854\n",
            "Sharpe:  1.002176631256902\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 345            |\n",
            "|    iterations           | 18             |\n",
            "|    time_elapsed         | 106            |\n",
            "|    total_timesteps      | 36864          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.0803342e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.15e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 8.41e+14       |\n",
            "|    n_updates            | 170            |\n",
            "|    policy_gradient_loss | -4.91e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 1.58e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4796634.5596691\n",
            "Sharpe:  1.0678319491053092\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 344            |\n",
            "|    iterations           | 19             |\n",
            "|    time_elapsed         | 112            |\n",
            "|    total_timesteps      | 38912          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -4.21e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.03e+15       |\n",
            "|    n_updates            | 180            |\n",
            "|    policy_gradient_loss | -5.6e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.02e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4969786.413399254\n",
            "Sharpe:  1.0823021486710163\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 344            |\n",
            "|    iterations           | 20             |\n",
            "|    time_elapsed         | 118            |\n",
            "|    total_timesteps      | 40960          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -6.7055225e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.41e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.22e+15       |\n",
            "|    n_updates            | 190            |\n",
            "|    policy_gradient_loss | -2.87e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.4e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4885480.801922398\n",
            "Sharpe:  1.0729451877791811\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 343            |\n",
            "|    iterations           | 21             |\n",
            "|    time_elapsed         | 125            |\n",
            "|    total_timesteps      | 43008          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -5.5879354e-09 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.85e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.62e+15       |\n",
            "|    n_updates            | 200            |\n",
            "|    policy_gradient_loss | -5.24e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.95e+15       |\n",
            "--------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 343           |\n",
            "|    iterations           | 22            |\n",
            "|    time_elapsed         | 131           |\n",
            "|    total_timesteps      | 45056         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.8067658e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.01e+19     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.34e+15      |\n",
            "|    n_updates            | 210           |\n",
            "|    policy_gradient_loss | -4.62e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.93e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5613709.009268909\n",
            "Sharpe:  1.1673870008513114\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 342            |\n",
            "|    iterations           | 23             |\n",
            "|    time_elapsed         | 137            |\n",
            "|    total_timesteps      | 47104          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.0489097e-08 |\n",
            "|    clip_fraction        | 0              |\n",
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            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.72e+19      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.41e+15       |\n",
            "|    n_updates            | 220            |\n",
            "|    policy_gradient_loss | -4.78e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.71e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5043800.590470289\n",
            "Sharpe:  1.0953673306850924\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 342           |\n",
            "|    iterations           | 24            |\n",
            "|    time_elapsed         | 143           |\n",
            "|    total_timesteps      | 49152         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.4214387e-08 |\n",
            "|    clip_fraction        | 0             |\n",
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            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.37e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.01e+15      |\n",
            "|    n_updates            | 230           |\n",
            "|    policy_gradient_loss | -5.28e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.26e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4776576.852863929\n",
            "Sharpe:  1.0593811754233755\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 342           |\n",
            "|    iterations           | 25            |\n",
            "|    time_elapsed         | 149           |\n",
            "|    total_timesteps      | 51200         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 4.4703484e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -3.27e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.21e+15      |\n",
            "|    n_updates            | 240           |\n",
            "|    policy_gradient_loss | -4.82e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.46e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4468393.200157898\n",
            "Sharpe:  1.0192746589767419\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 341           |\n",
            "|    iterations           | 26            |\n",
            "|    time_elapsed         | 156           |\n",
            "|    total_timesteps      | 53248         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.6077032e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.96e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.31e+15      |\n",
            "|    n_updates            | 250           |\n",
            "|    policy_gradient_loss | -5.36e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.59e+15      |\n",
            "-------------------------------------------\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 341            |\n",
            "|    iterations           | 27             |\n",
            "|    time_elapsed         | 162            |\n",
            "|    total_timesteps      | 55296          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.3038516e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.68e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.33e+15       |\n",
            "|    n_updates            | 260            |\n",
            "|    policy_gradient_loss | -3.77e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.51e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4875234.39450474\n",
            "Sharpe:  1.0721137742534572\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 340            |\n",
            "|    iterations           | 28             |\n",
            "|    time_elapsed         | 168            |\n",
            "|    total_timesteps      | 57344          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -1.2479722e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.66e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.59e+15       |\n",
            "|    n_updates            | 270            |\n",
            "|    policy_gradient_loss | -4.61e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.8e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4600459.210918712\n",
            "Sharpe:  1.034756153745345\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 340           |\n",
            "|    iterations           | 29            |\n",
            "|    time_elapsed         | 174           |\n",
            "|    total_timesteps      | 59392         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | -4.284084e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.26e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 8.07e+14      |\n",
            "|    n_updates            | 280           |\n",
            "|    policy_gradient_loss | -5.44e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.62e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4526188.381438201\n",
            "Sharpe:  1.0293846869900876\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 30             |\n",
            "|    time_elapsed         | 180            |\n",
            "|    total_timesteps      | 61440          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.4214387e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -6.44e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.12e+15       |\n",
            "|    n_updates            | 290            |\n",
            "|    policy_gradient_loss | -5.65e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.1e+15        |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4487836.803716703\n",
            "Sharpe:  1.010974660894394\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 31             |\n",
            "|    time_elapsed         | 187            |\n",
            "|    total_timesteps      | 63488          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.6077032e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -4.47e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.14e+15       |\n",
            "|    n_updates            | 300            |\n",
            "|    policy_gradient_loss | -4.8e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.25e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4480729.650671386\n",
            "Sharpe:  1.0219085518652522\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 339            |\n",
            "|    iterations           | 32             |\n",
            "|    time_elapsed         | 193            |\n",
            "|    total_timesteps      | 65536          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -2.0302832e-07 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -3.87e+20      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.28e+15       |\n",
            "|    n_updates            | 310            |\n",
            "|    policy_gradient_loss | -4.4e-07       |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.51e+15       |\n",
            "--------------------------------------------\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 339          |\n",
            "|    iterations           | 33           |\n",
            "|    time_elapsed         | 199          |\n",
            "|    total_timesteps      | 67584        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 1.359731e-07 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -3.68e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.24e+15     |\n",
            "|    n_updates            | 320          |\n",
            "|    policy_gradient_loss | -4.51e-07    |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.66e+15     |\n",
            "------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4399373.734699048\n",
            "Sharpe:  1.005407087483561\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 34            |\n",
            "|    time_elapsed         | 205           |\n",
            "|    total_timesteps      | 69632         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 2.2351742e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -2.29e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 8.5e+14       |\n",
            "|    n_updates            | 330           |\n",
            "|    policy_gradient_loss | -5.56e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.64e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4305742.921261859\n",
            "Sharpe:  0.9945061913961891\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 35            |\n",
            "|    time_elapsed         | 211           |\n",
            "|    total_timesteps      | 71680         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.3411045e-07 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.11e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 7.97e+14      |\n",
            "|    n_updates            | 340           |\n",
            "|    policy_gradient_loss | -6.48e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.8e+15       |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4794175.629957249\n",
            "Sharpe:  1.0611635246548963\n",
            "=================================\n",
            "--------------------------------------------\n",
            "| time/                   |                |\n",
            "|    fps                  | 338            |\n",
            "|    iterations           | 36             |\n",
            "|    time_elapsed         | 217            |\n",
            "|    total_timesteps      | 73728          |\n",
            "| train/                  |                |\n",
            "|    approx_kl            | -3.3527613e-08 |\n",
            "|    clip_fraction        | 0              |\n",
            "|    clip_range           | 0.2            |\n",
            "|    entropy_loss         | -42.6          |\n",
            "|    explained_variance   | -1.16e+21      |\n",
            "|    learning_rate        | 0.0001         |\n",
            "|    loss                 | 1.07e+15       |\n",
            "|    n_updates            | 350            |\n",
            "|    policy_gradient_loss | -4.82e-07      |\n",
            "|    std                  | 1              |\n",
            "|    value_loss           | 2.06e+15       |\n",
            "--------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4467487.416264421\n",
            "Sharpe:  1.021012208464475\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 338          |\n",
            "|    iterations           | 37           |\n",
            "|    time_elapsed         | 224          |\n",
            "|    total_timesteps      | 75776        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 5.401671e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -9.89e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.46e+15     |\n",
            "|    n_updates            | 360          |\n",
            "|    policy_gradient_loss | -4.78e-07    |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.75e+15     |\n",
            "------------------------------------------\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 38            |\n",
            "|    time_elapsed         | 229           |\n",
            "|    total_timesteps      | 77824         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 1.6763806e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -7.64e+20     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.25e+15      |\n",
            "|    n_updates            | 370           |\n",
            "|    policy_gradient_loss | -4.54e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 2.57e+15      |\n",
            "-------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4806649.219027834\n",
            "Sharpe:  1.0604486398186765\n",
            "=================================\n",
            "------------------------------------------\n",
            "| time/                   |              |\n",
            "|    fps                  | 338          |\n",
            "|    iterations           | 39           |\n",
            "|    time_elapsed         | 236          |\n",
            "|    total_timesteps      | 79872        |\n",
            "| train/                  |              |\n",
            "|    approx_kl            | 4.284084e-08 |\n",
            "|    clip_fraction        | 0            |\n",
            "|    clip_range           | 0.2          |\n",
            "|    entropy_loss         | -42.6        |\n",
            "|    explained_variance   | -6.96e+20    |\n",
            "|    learning_rate        | 0.0001       |\n",
            "|    loss                 | 1.28e+15     |\n",
            "|    n_updates            | 380          |\n",
            "|    policy_gradient_loss | -5.9e-07     |\n",
            "|    std                  | 1            |\n",
            "|    value_loss           | 2.44e+15     |\n",
            "------------------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4653147.508966551\n",
            "Sharpe:  1.043189911078732\n",
            "=================================\n",
            "-------------------------------------------\n",
            "| time/                   |               |\n",
            "|    fps                  | 338           |\n",
            "|    iterations           | 40            |\n",
            "|    time_elapsed         | 242           |\n",
            "|    total_timesteps      | 81920         |\n",
            "| train/                  |               |\n",
            "|    approx_kl            | 6.3329935e-08 |\n",
            "|    clip_fraction        | 0             |\n",
            "|    clip_range           | 0.2           |\n",
            "|    entropy_loss         | -42.6         |\n",
            "|    explained_variance   | -1.04e+21     |\n",
            "|    learning_rate        | 0.0001        |\n",
            "|    loss                 | 1.01e+15      |\n",
            "|    n_updates            | 390           |\n",
            "|    policy_gradient_loss | -5.33e-07     |\n",
            "|    std                  | 1             |\n",
            "|    value_loss           | 1.82e+15      |\n",
            "-------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3Iuv554xYFH"
      },
      "source": [
        "### Model 3: **DDPG**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ojmppgo4LPLz",
        "outputId": "bb2459a2-3231-481e-9628-4ace4256f9e1"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "DDPG_PARAMS = {\"batch_size\": 128, \"buffer_size\": 50000, \"learning_rate\": 0.001}\n",
        "\n",
        "\n",
        "model_ddpg = agent.get_model(\"ddpg\",model_kwargs = DDPG_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWt6BIR0LT25",
        "outputId": "d523d157-f2c7-4667-9971-214ddc51fcb1"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_2\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4625995.900359718\n",
            "Sharpe:  1.040202670783119\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 22        |\n",
            "|    time_elapsed    | 439       |\n",
            "|    total timesteps | 10064     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -6.99e+07 |\n",
            "|    critic_loss     | 7.27e+12  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 7548      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 20        |\n",
            "|    time_elapsed    | 980       |\n",
            "|    total timesteps | 20128     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.44e+08 |\n",
            "|    critic_loss     | 1.81e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 17612     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 19        |\n",
            "|    time_elapsed    | 1542      |\n",
            "|    total timesteps | 30192     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.88e+08 |\n",
            "|    critic_loss     | 2.72e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 27676     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 18        |\n",
            "|    time_elapsed    | 2133      |\n",
            "|    total timesteps | 40256     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -2.15e+08 |\n",
            "|    critic_loss     | 3.45e+13  |\n",
            "|    learning_rate   | 0.001     |\n",
            "|    n_updates       | 37740     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4450723.86820311\n",
            "Sharpe:  1.008267759668747\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 17       |\n",
            "|    time_elapsed    | 2874     |\n",
            "|    total timesteps | 50320    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -2.3e+08 |\n",
            "|    critic_loss     | 4.05e+13 |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 47804    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPEXBcm-uBJo"
      },
      "source": [
        "### Model 4: **SAC**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaWf2QeiLqyO",
        "outputId": "f74b3a46-6195-43f3-d321-512a3318f767"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 100000,\n",
        "    \"learning_rate\": 0.0003,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 100000, 'learning_rate': 0.0003, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgYVPqtKLvi3",
        "outputId": "d633cfb8-2379-485c-9c2b-5737f0fa2b23"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_1\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4449463.498168942\n",
            "Sharpe:  1.01245667390232\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418643.239765096\n",
            "Sharpe:  1.0135796594260282\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418644.1960784905\n",
            "Sharpe:  1.0135797537524718\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418659.429680678\n",
            "Sharpe:  1.013581852537709\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 4         |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 783       |\n",
            "|    total timesteps | 10064     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -8.83e+07 |\n",
            "|    critic_loss     | 6.57e+12  |\n",
            "|    ent_coef        | 2.24      |\n",
            "|    ent_coef_loss   | -205      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 9963      |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418651.576406099\n",
            "Sharpe:  1.013581224026754\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418670.948269031\n",
            "Sharpe:  1.0135838030234754\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418682.278829884\n",
            "Sharpe:  1.013585596968056\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418791.911955293\n",
            "Sharpe:  1.0136007328171013\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 8         |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 1585      |\n",
            "|    total timesteps | 20128     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.51e+08 |\n",
            "|    critic_loss     | 1.12e+13  |\n",
            "|    ent_coef        | 41.7      |\n",
            "|    ent_coef_loss   | -670      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 20027     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418737.365107464\n",
            "Sharpe:  1.0135970410224868\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418754.895735274\n",
            "Sharpe:  1.0135965589029627\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4419325.814567342\n",
            "Sharpe:  1.0136807224228588\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4418142.473513333\n",
            "Sharpe:  1.0135234795926031\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 12        |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 2400      |\n",
            "|    total timesteps | 30192     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.85e+08 |\n",
            "|    critic_loss     | 1.87e+13  |\n",
            "|    ent_coef        | 725       |\n",
            "|    ent_coef_loss   | -673      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 30091     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4422046.188863339\n",
            "Sharpe:  1.0140936726052256\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4424919.463828854\n",
            "Sharpe:  1.014521127041106\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4427483.152494239\n",
            "Sharpe:  1.0148626804754584\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4460697.650185859\n",
            "Sharpe:  1.019852362102548\n",
            "=================================\n",
            "----------------------------------\n",
            "| time/              |           |\n",
            "|    episodes        | 16        |\n",
            "|    fps             | 12        |\n",
            "|    time_elapsed    | 3210      |\n",
            "|    total timesteps | 40256     |\n",
            "| train/             |           |\n",
            "|    actor_loss      | -1.93e+08 |\n",
            "|    critic_loss     | 1.62e+13  |\n",
            "|    ent_coef        | 1.01e+04  |\n",
            "|    ent_coef_loss   | -238      |\n",
            "|    learning_rate   | 0.0003    |\n",
            "|    n_updates       | 40155     |\n",
            "----------------------------------\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4434035.982803257\n",
            "Sharpe:  1.0161512551319891\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4454728.906041551\n",
            "Sharpe:  1.018484863448905\n",
            "=================================\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4475667.120269234\n",
            "Sharpe:  1.0215545521682856\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2Ma6YpTlnuZ"
      },
      "source": [
        "## Trading\r\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uas8U6k455sI"
      },
      "source": [
        "trade = data_split(df,'2019-01-01', '2021-01-01')\n",
        "e_trade_gym = StockPortfolioEnv(df = trade, **env_kwargs)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcGYlhyal205",
        "outputId": "babe3aae-8fb2-40ba-a864-e43535f23bb5"
      },
      "source": [
        "trade.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15150, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qq4W9FbSstT7",
        "outputId": "d84bf2b3-7f86-4ab5-ea9c-73aa4b2b00d3"
      },
      "source": [
        "df_daily_return, df_actions = DRLAgent.DRL_prediction(model=trained_a2c,\r\n",
        "                        environment = e_trade_gym)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1362301.0314001127\n",
            "Sharpe:  0.7071658501639263\n",
            "=================================\n",
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uJvj3pXt_Ukg",
        "outputId": "b67bec5c-a649-46d2-dab2-1169f053bc0c"
      },
      "source": [
        "df_daily_return.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>-0.023322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>0.031476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>0.003001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>0.010326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  daily_return\n",
              "0  2019-01-02      0.000000\n",
              "1  2019-01-03     -0.023322\n",
              "2  2019-01-04      0.031476\n",
              "3  2019-01-07      0.003001\n",
              "4  2019-01-08      0.010326"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "tByVcZ2L9TAJ",
        "outputId": "9de22b01-8fd4-411d-d993-d89967be95ee"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-01-02</th>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.033333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-03</th>\n",
              "      <td>0.028414</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.032319</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.036182</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.050456</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.058128</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.029581</td>\n",
              "      <td>0.023299</td>\n",
              "      <td>0.028871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-04</th>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.061633</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.061633</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.061633</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.023052</td>\n",
              "      <td>0.061633</td>\n",
              "      <td>0.036312</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.030031</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.044044</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.025697</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.061633</td>\n",
              "      <td>0.061113</td>\n",
              "      <td>0.024500</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.022674</td>\n",
              "      <td>0.061633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-07</th>\n",
              "      <td>0.028707</td>\n",
              "      <td>0.057629</td>\n",
              "      <td>0.041837</td>\n",
              "      <td>0.057885</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.033156</td>\n",
              "      <td>0.054076</td>\n",
              "      <td>0.024508</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.057885</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.057885</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.029233</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.026089</td>\n",
              "      <td>0.030883</td>\n",
              "      <td>0.045760</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.057885</td>\n",
              "      <td>0.057885</td>\n",
              "      <td>0.026866</td>\n",
              "      <td>0.021295</td>\n",
              "      <td>0.034995</td>\n",
              "      <td>0.021295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-01-08</th>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.034963</td>\n",
              "      <td>0.021315</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.023460</td>\n",
              "      <td>0.022357</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.033571</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.044475</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.037073</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.043909</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.019370</td>\n",
              "      <td>0.052653</td>\n",
              "      <td>0.032565</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                AAPL       AXP        BA  ...       WBA       WMT       XOM\n",
              "date                                      ...                              \n",
              "2019-01-02  0.033333  0.033333  0.033333  ...  0.033333  0.033333  0.033333\n",
              "2019-01-03  0.028414  0.063333  0.023299  ...  0.029581  0.023299  0.028871\n",
              "2019-01-04  0.022674  0.022674  0.022674  ...  0.022674  0.022674  0.061633\n",
              "2019-01-07  0.028707  0.057629  0.041837  ...  0.021295  0.034995  0.021295\n",
              "2019-01-08  0.052653  0.034963  0.021315  ...  0.019370  0.052653  0.032565\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBX3Y68o1vRG"
      },
      "source": [
        "df_actions.to_csv('df_actions.csv')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFO42LcomPUT"
      },
      "source": [
        "<a id='6'></a>\r\n",
        "# Part 7: Backtest Our Strategy\r\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAvxipWFmUe8"
      },
      "source": [
        "<a id='6.1'></a>\r\n",
        "## 7.1 BackTestStats\r\n",
        "pass in df_account_value, this information is stored in env class\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oGu3PCa8l6L"
      },
      "source": [
        "from pyfolio import timeseries\n",
        "DRL_strat = convert_daily_return_to_pyfolio_ts(df_daily_return)\n",
        "perf_func = timeseries.perf_stats \n",
        "perf_stats_all = perf_func( returns=DRL_strat, \n",
        "                              factor_returns=DRL_strat, \n",
        "                                positions=None, transactions=None, turnover_denom=\"AGB\")"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqvwr6SY8l9A",
        "outputId": "d6cb4978-9c3a-4a6a-daf2-12c6e1ef1486"
      },
      "source": [
        "print(\"==============DRL Strategy Stats===========\")\r\n",
        "perf_stats_all"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============DRL Strategy Stats===========\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Annual return           0.166819\n",
              "Cumulative returns      0.362301\n",
              "Annual volatility       0.269856\n",
              "Sharpe ratio            0.707166\n",
              "Calmar ratio            0.472240\n",
              "Stability               0.208610\n",
              "Max drawdown           -0.353251\n",
              "Omega ratio             1.164703\n",
              "Sortino ratio           0.996437\n",
              "Skew                   -0.277785\n",
              "Kurtosis               12.301202\n",
              "Tail ratio              0.821009\n",
              "Daily value at risk    -0.033241\n",
              "Alpha                   0.000000\n",
              "Beta                    1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVVCMVSAmcrI"
      },
      "source": [
        "<a id='6.2'></a>\r\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2fqSEF5PfjjT",
        "outputId": "2e8f9c39-812e-4020-d732-7a086c627f62"
      },
      "source": [
        "import pyfolio\r\n",
        "%matplotlib inline\r\n",
        "\r\n",
        "baseline_df = get_baseline(\r\n",
        "        ticker='^DJI', start='2019-01-01', end='2021-01-01'\r\n",
        "    )\r\n",
        "\r\n",
        "baseline_returns = get_daily_return(baseline_df, value_col_name=\"close\")\r\n",
        "\r\n",
        "with pyfolio.plotting.plotting_context(font_scale=1.1):\r\n",
        "        pyfolio.create_full_tear_sheet(returns = DRL_strat,\r\n",
        "                                       benchmark_rets=baseline_returns, set_context=False)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (505, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-02</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-31</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>24</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>16.682%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>36.23%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>26.986%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-35.325%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>12.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-3.324%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.33</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-11-24</td>\n",
              "      <td>205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.91</td>\n",
              "      <td>2019-07-29</td>\n",
              "      <td>2019-08-23</td>\n",
              "      <td>2019-10-25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.48</td>\n",
              "      <td>2019-04-30</td>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>2019-06-18</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.74</td>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.33</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2DgsIW-fh0s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}